Gastos Casa
16 octubre, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 9/8/2023 | Agua | 11520 | Andrés | NA |
| 15/8/2023 | Comida | 51910 | Tami | Supermercado |
| 16/8/2023 | Bencina + peajes Maite | 49000 | Tami | NA |
| 16/8/2023 | Comida | 13500 | Tami | Maitemarket |
| 20/8/2023 | VTR | 21990 | Andrés | NA |
| 21/8/2023 | Comida | 99535 | Tami | NA |
| 21/8/2023 | Comida | 27680 | Andrés | nueces almendras |
| 26/8/2023 | Comida | 71467 | Tami | Supermercado |
| 31/8/2023 | Netflix | 5940 | Tami | NA |
| 1/9/2023 | Comida | 94874 | Tami | Supermercado |
| 10/9/2023 | Comida | 85445 | Tami | Supermercado |
| 11/9/2023 | Agua | 10332 | Andrés | NA |
| 16/9/2023 | Comida | 79913 | Tami | Supermercado |
| 16/9/2023 | Enceres | 14400 | Tami | Incoludido |
| 16/9/2023 | Comida | 18580 | Andrés | Johnny Rockets |
| 16/9/2023 | Comida | 38151 | Andrés | Frutos secos |
| 16/9/2023 | Diosi | 21081 | Andrés | antiparasitario |
| 17/9/2023 | Diosi | 8000 | Andrés | arena |
| 18/9/2023 | Comida | 10000 | Andrés | empanadas (3) menos helado q me comí |
| 20/9/2023 | VTR | 21990 | Andrés | NA |
| 16/9/2023 | Comida | 27980 | Tami | Cajas Soul Bar |
| 23/9/2023 | Comida | 57639 | Tami | Supermercado |
| 24/9/2023 | Diosi | 8000 | Andrés | arena diosi 10kg |
| 30/9/2023 | Electricidad | 44407 | Andrés | NA |
| 30/9/2023 | Comida | 51726 | Tami | Supermercado |
| 6/10/2023 | Comida | 44298 | Tami | Supermercado |
| 14/10/2023 | Comida | 86673 | Tami | Supermercado |
| 10/10/2023 | Diosi | 6880 | Tami | Omega aceite petsu |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 7.8533e+08 2 7.8488 4e-04 ***
## lag_depvar 8.5323e+10 1 1705.4750 <2e-16 ***
## Residuals 3.1218e+10 624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1132.649 13325.03 0.0151776
## 2-0 28739.489 23192.624 34286.35 0.0000000
## 2-1 21510.651 18230.927 24790.37 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
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## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
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## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 472 50973.75 14938.021
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1917.25661 3996.82270 -494.80814 2475.57735 -2884.08022 549.84165
## 8 9 10 11 12 13
## -5610.12857 -1239.31771 -4023.01124 -528.99521 -5034.88972 -1771.22498
## 14 15 16 17 18 19
## -1060.50857 230.29397 -3355.44212 -524.68548 -2255.77174 6465.71790
## 20 21 22 23 24 25
## -1523.43124 -1221.21845 1452.07631 -1171.64478 235.86762 1709.53272
## 26 27 28 29 30 31
## -7049.87556 876.11700 8156.25426 541.91733 110.98872 -2282.19178
## 32 33 34 35 36 37
## 1646.34243 4671.28296 1304.08208 2575.77879 -1654.88458 4770.35967
## 38 39 40 41 42 43
## 4399.55958 -2114.26421 -2880.05209 -1073.81706 -10728.67227 7110.09223
## 44 45 46 47 48 49
## 2531.11592 1390.29595 8150.76701 871.07042 6703.36300 6983.99269
## 50 51 52 53 54 55
## -5526.41518 -4588.31349 -4963.28353 -7933.54827 5985.12669 -4093.16690
## 56 57 58 59 60 61
## -4980.58430 3694.07643 815.88163 -78.43639 101.93135 -5028.35466
## 62 63 64 65 66 67
## 18010.71655 3862.64700 -3386.51886 6088.14589 7592.62007 14987.72685
## 68 69 70 71 72 73
## 2259.54801 -12686.72759 -1080.67237 4818.21908 -4664.08509 -4284.40774
## 74 75 76 77 78 79
## -10469.89574 2305.28750 -5496.05468 884.61498 -7002.94499 306.40833
## 80 81 82 83 84 85
## -2554.07584 -2909.09475 -4166.87001 -811.77119 2066.34151 3587.70682
## 86 87 88 89 90 91
## 391.62565 -549.84261 131.69850 4249.46212 -1131.69568 1158.32575
## 92 93 94 95 96 97
## -2036.72261 -1055.93547 149.64520 254.30511 -7496.27311 2249.04005
## 98 99 100 101 102 103
## -8683.87992 -3163.40726 -4285.31335 -2022.14741 -1540.47014 2915.71551
## 104 105 106 107 108 109
## -2516.46093 2400.95532 -1280.22799 844.30788 2494.91379 -3188.21820
## 110 111 112 113 114 115
## -4807.74450 -1007.18885 1752.19298 11595.93887 -1119.60916 2754.76554
## 116 117 118 119 120 121
## 4386.38757 3687.14078 -875.72321 -4539.03429 -3651.95689 2317.63563
## 122 123 124 125 126 127
## -1692.45897 1345.69705 8887.52735 1030.75337 306.84129 -2363.93291
## 128 129 130 131 132 133
## 2748.69386 7182.32783 1251.88879 -8271.33482 1798.52663 4210.50080
## 134 135 136 137 138 139
## -3024.20284 -1352.91232 -819.92953 -3864.55224 1128.57259 -521.22715
## 140 141 142 143 144 145
## -2944.04300 1640.72493 -1917.42158 -7893.59373 1845.21942 -3612.43649
## 146 147 148 149 150 151
## 1925.32160 -373.99228 917.41101 -432.68107 1282.46273 1150.44961
## 152 153 154 155 156 157
## 3346.77442 -4810.04275 -1214.71258 -3291.01290 5851.87850 9761.48240
## 158 159 160 161 162 163
## -3511.69043 -4891.81907 3437.28568 130.05255 2660.00065 -5877.72914
## 164 165 166 167 168 169
## -6806.52667 4002.66293 17347.72712 3904.56471 -78.65553 -2158.25177
## 170 171 172 173 174 175
## -879.99836 3782.38702 16.83525 -7850.25061 2935.28752 4452.66099
## 176 177 178 179 180 181
## 826.47858 8951.58462 -8906.65006 -3318.59330 -10656.22835 -11334.26164
## 182 183 184 185 186 187
## 972.81384 9100.58615 -1423.08794 5924.27934 6667.26198 13381.00776
## 188 189 190 191 192 193
## 8861.58317 -3533.24323 2867.11314 10771.12815 -1106.75043 -1995.00158
## 194 195 196 197 198 199
## -9920.42253 -6213.71982 1266.90903 -5171.69483 -9819.27168 5209.52196
## 200 201 202 203 204 205
## -3115.58450 -1792.94250 -890.59483 6417.93620 9935.81757 800.78290
## 206 207 208 209 210 211
## 3138.18144 3343.37822 6060.63242 13182.02032 -5157.74854 -10919.55116
## 212 213 214 215 216 217
## -5516.78707 -10540.38051 -5199.77324 1345.80692 -13129.58282 16080.18318
## 218 219 220 221 222 223
## 7827.19884 1685.58956 26861.91033 13132.54619 8080.92812 14807.85739
## 224 225 226 227 228 229
## -2990.02914 -992.47812 4410.21352 985.04283 3308.05828 9552.32621
## 230 231 232 233 234 235
## 6471.98723 -1234.79299 -1261.66228 9901.28821 -10923.58932 -6955.06002
## 236 237 238 239 240 241
## -8365.49140 -10079.92704 2938.91138 1295.32604 -8311.39632 -9128.52570
## 242 243 244 245 246 247
## 8835.77115 -7826.27320 2316.78730 -10395.72750 -4296.12224 1156.35706
## 248 249 250 251 252 253
## 806.92364 -12458.06978 3326.04519 1858.60143 4079.96119 2101.04612
## 254 255 256 257 258 259
## -1143.62904 11145.33453 21080.33346 3722.20155 -3753.98663 4497.52695
## 260 261 262 263 264 265
## -1280.74038 4078.13145 -4484.73109 -10645.13342 -4682.35173 -549.63777
## 266 267 268 269 270 271
## -5211.56217 8681.94394 -4211.97162 4187.08739 -2032.51306 4469.15500
## 272 273 274 275 276 277
## 822.19097 7420.39421 -1184.54311 12206.65019 -4226.82229 1967.02308
## 278 279 280 281 282 283
## -128.65564 8063.17782 -4741.42597 -2531.02525 -11123.48377 -2720.52154
## 284 285 286 287 288 289
## 18576.73829 8020.38039 3069.86009 -287.53090 1198.39415 6672.73357
## 290 291 292 293 294 295
## 7228.11591 -18357.45082 -11065.36758 -8221.54104 9462.51314 3059.02720
## 296 297 298 299 300 301
## -1132.10181 27434.33175 10528.88214 5466.93729 10092.66191 3510.51566
## 302 303 304 305 306 307
## -413.16375 8425.54012 -23705.80009 -3387.73618 -90.36547 -6885.12930
## 308 309 310 311 312 313
## -3994.18534 2865.29831 -9191.54633 -3355.86986 -8329.14598 1332.61018
## 314 315 316 317 318 319
## -3313.23459 1875.01710 -4186.95325 27306.65321 -434.21532 3539.92805
## 320 321 322 323 324 325
## 11104.59135 5999.59630 32829.81749 6001.61261 -20076.55936 2232.80040
## 326 327 328 329 330 331
## 1530.43440 -6072.94643 -1480.21757 -33061.25008 609.52092 -2501.01534
## 332 333 334 335 336 337
## -276.02215 -3304.04767 3943.62598 -476.56008 -6970.90178 -3212.96972
## 338 339 340 341 342 343
## -2298.12606 -7780.82637 3673.72469 -1445.66392 -1799.92415 -1050.62456
## 344 345 346 347 348 349
## 135.54147 471.09968 -1598.68521 -9431.89066 -13315.27941 2046.26648
## 350 351 352 353 354 355
## -4495.21796 -3848.32211 -6175.15622 1516.81090 1229.33885 2659.94648
## 356 357 358 359 360 361
## -3787.42477 -569.97322 642.97498 7013.05133 396.16711 87.46108
## 362 363 364 365 366 367
## 2709.54751 -2585.15776 -754.82474 -8629.92066 -4631.37412 -6260.70215
## 368 369 370 371 372 373
## -5057.06839 -7393.08407 4813.05519 289.05849 7074.89014 -7553.77474
## 374 375 376 377 378 379
## -2278.95321 -3411.61731 -2513.06104 -12509.47628 1703.74688 -10756.45780
## 380 381 382 383 384 385
## 5461.91852 9235.97309 3191.31831 -2279.32825 1692.73795 6861.48973
## 386 387 388 389 390 391
## 11624.41590 -5442.61489 -5122.36747 -11.86990 8701.85541 2069.11514
## 392 393 394 395 396 397
## 11479.78358 -9486.82993 2983.19106 944.09034 784.32119 -442.01177
## 398 399 400 401 402 403
## -376.90568 -14321.54113 8483.45129 -1074.69355 -1278.54347 7062.54387
## 404 405 406 407 408 409
## -7747.81581 -1228.07007 -2468.57606 -5777.30102 -2879.59221 -3950.12165
## 410 411 412 413 414 415
## -8813.58019 5986.69905 1621.38590 -7345.41928 -7747.15887 14091.06552
## 416 417 418 419 420 421
## 3918.29593 4648.54635 -7823.28230 -4652.05598 -2564.60848 2838.65007
## 422 423 424 425 426 427
## -13933.66425 -2894.37111 -9201.42069 2824.12357 6879.35107 6608.27460
## 428 429 430 431 432 433
## -3848.90579 -4034.74757 -4682.05061 -1796.71502 -5719.28203 -6688.23507
## 434 435 436 437 438 439
## -6070.26146 -1555.31895 -982.75517 -5079.44484 2446.75793 4781.88530
## 440 441 442 443 444 445
## -5017.43095 -2171.28895 1558.55109 -3807.76714 2831.04394 -6519.99579
## 446 447 448 449 450 451
## -12128.06480 -4670.47488 9470.10606 -2024.36224 4757.16180 -5776.77958
## 452 453 454 455 456 457
## -1099.85646 412.61731 3081.07283 -12154.67017 3320.09810 -6671.69334
## 458 459 460 461 462 463
## 6478.57842 3090.52199 2640.83975 -3671.34400 2213.46360 150.17833
## 464 465 466 467 468 469
## 1952.67277 -334.59258 3527.76493 -2415.73291 5985.16300 -6681.07232
## 470 471 472 473 474 475
## -2810.70373 -2087.94764 -4566.77993 3041.40761 7904.34424 -5789.84306
## 476 477 478 479 480 481
## 1621.68485 -6013.22658 -2761.92405 2069.82390 -12825.07645 -9821.55135
## 482 483 484 485 486 487
## -1371.76848 -116.59670 -1053.66578 -1408.14739 -9634.70655 10941.99077
## 488 489 490 491 492 493
## 6286.73048 7578.91606 -5169.15715 5546.53938 9549.07479 6431.24126
## 494 495 496 497 498 499
## -13037.71284 -10355.11833 -3382.27482 -1076.72034 -487.74871 -7574.66704
## 500 501 502 503 504 505
## 574.71039 4292.86447 5601.59217 845.95380 277.03661 -7041.31629
## 506 507 508 509 510 511
## 664.88531 -4930.74121 1889.70851 -1193.38984 -8059.90583 -606.42177
## 512 513 514 515 516 517
## -2659.18451 -585.10382 1353.65295 -9430.18016 -7817.55585 24157.43571
## 518 519 520 521 522 523
## 10087.54694 6271.83531 -4887.66196 3134.34399 17374.34950 12047.13064
## 524 525 526 527 528 529
## -23466.82254 -4790.51300 -3547.39560 4705.18476 -151.39314 -10905.31052
## 530 531 532 533 534 535
## 4429.43262 14030.98090 -4648.83252 4610.32194 5845.75343 -1436.43132
## 536 537 538 539 540 541
## -4236.67266 -6851.07052 -1982.77784 8420.74155 356.61017 -7915.94376
## 542 543 544 545 546 547
## 1920.82362 -456.77001 507.38271 -10877.43165 -11059.70400 1911.71759
## 548 549 550 551 552 553
## 6948.44059 -1238.26451 913.56572 -7615.62817 8572.60783 1062.46160
## 554 555 556 557 558 559
## -11770.31198 9168.37952 8819.37683 386.84724 5127.77520 -3243.54563
## 560 561 562 563 564 565
## 14367.70316 21947.14256 -5751.23442 -9132.58536 7134.09079 656.13408
## 566 567 568 569 570 571
## 3852.64845 -6960.67818 -17028.17740 6685.73264 6577.30866 2144.33462
## 572 573 574 575 576 577
## 3361.22769 2067.94045 -1855.72778 14979.91828 -9186.69354 -5958.42274
## 578 579 580 581 582 583
## 8894.98880 3167.25810 -6212.34906 7729.14086 -3476.28366 -2524.23062
## 584 585 586 587 588 589
## 15906.64060 -14072.76115 8614.50280 378.22901 -5918.89478 -562.17805
## 590 591 592 593 594 595
## 434.75753 -10463.90068 1836.15484 -7056.13726 3070.29094 8941.32353
## 596 597 598 599 600 601
## -7280.70348 5961.73324 2942.00816 7101.52373 -2852.06919 6423.45961
## 602 603 604 605 606 607
## -7945.78419 2468.93954 1515.28773 3396.77958 1791.96403 703.69521
## 608 609 610 611 612 613
## -5511.68973 8278.18252 -862.80861 -2284.74116 -3210.76392 -8038.51583
## 614 615 616 617 618 619
## 12034.09757 5204.41054 -8981.89860 11808.91867 6405.90020 -5147.81608
## 620 621 622 623 624 625
## 26677.04152 -12136.15122 -6337.81669 3472.11427 -3809.99759 -10319.93465
## 626 627 628 629
## 11408.44583 -21342.68434 -2448.35078 8641.90473
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17352.03 20142.18 24310.95 24034.57 26340.79 23726.87 24428.84 19756.46
## 10 11 12 13 14 15 16 17
## 19498.30 16894.28 17656.18 14451.08 14501.22 15152.56 16815.16 15168.83
## 18 19 20 21 22 23 24 25
## 16182.77 15568.85 22509.43 21611.79 21102.07 22954.22 22293.70 22933.18
## 26 27 28 29 30 31 32 33
## 24742.16 18792.17 20483.75 28164.08 28220.58 27900.05 25576.94 26951.29
## 34 35 36 37 38 39 40 41
## 30717.35 31058.79 32439.74 30000.21 34043.44 37187.26 34302.34 31177.10
## 42 43 44 45 46 47 48 49
## 30047.96 20816.19 28184.31 30571.99 31639.38 38340.50 37845.21 42414.01
## 50 51 52 53 54 55 56 57
## 46565.42 39409.60 34086.85 29209.26 22491.02 28655.02 25304.16 21675.92
## 58 59 60 61 62 63 64 65
## 25995.98 27230.29 27521.35 27924.93 23878.57 40137.50 41944.52 37285.71
## 66 67 68 69 70 71 72 73
## 41408.38 46225.56 56680.02 54733.58 40272.39 37828.21 40785.66 35199.98
## 74 75 76 77 78 79 80 81
## 30743.32 21633.00 24770.34 20777.67 22821.94 17819.73 19794.79 19036.81
## 82 83 84 85 86 87 88 89
## 18084.01 16191.63 17443.80 20979.58 25308.80 26278.84 26303.30 26907.68
## 90 91 92 93 94 95 96 97
## 30950.12 29804.10 30783.44 28886.65 28102.50 28463.27 28861.70 22567.82
## 98 99 100 101 102 103 104 105
## 25522.45 18692.55 17571.60 15651.58 15945.33 16609.14 20992.18 20094.04
## 106 107 108 109 110 111 112 113
## 23534.80 23328.98 24971.51 27790.65 25338.89 21853.62 22123.52 24716.78
## 114 115 116 117 118 119 120 121
## 35363.61 33592.66 35393.33 38331.57 40248.29 37983.03 32907.81 29322.51
## 122 123 124 125 126 127 128 129
## 31363.60 29678.02 30835.90 38283.39 37933.02 37013.36 33939.73 35685.24
## 130 131 132 133 134 135 136 137
## 40974.97 40426.48 31804.47 33043.93 36169.77 32652.34 31071.93 30175.27
## 138 139 140 141 142 143 144 145
## 26801.28 28187.37 27961.61 25694.28 27678.14 26330.45 20060.78 23030.58
## 146 147 148 149 150 151 152 153
## 20900.82 23818.28 24347.45 25905.97 26084.39 27705.41 28980.08 31951.47
## 154 155 156 157 158 159 160 161
## 27512.43 26790.16 24394.41 30170.37 41532.12 39895.82 37313.57 42233.23
## 162 163 164 165 166 167 168 169
## 43613.57 46961.01 42517.81 37919.05 43235.56 59211.01 61378.80 59824.68
## 170 171 172 173 174 175 176 177
## 56714.00 55145.33 57793.74 56837.39 49284.00 52050.91 55718.52 55753.99
## 178 179 180 181 182 183 184 185
## 62739.94 53432.59 50248.66 41241.55 32950.47 36388.41 46289.37 45756.29
## 186 187 188 189 190 191 192 193
## 51589.74 57219.56 67786.42 72963.39 66784.46 66974.01 73902.61 69665.72
## 194 195 196 197 198 199 200 201
## 65278.28 54737.72 48887.52 50283.27 45966.27 38292.05 44588.01 42850.94
## 202 203 204 205 206 207 208 209
## 42496.17 42964.92 49622.75 58333.79 57970.82 59661.05 61283.65 64998.84
## 210 211 212 213 214 215 216 217
## 74275.61 66517.12 54942.93 49659.81 40836.63 37855.34 40906.58 31126.82
## 218 219 220 221 222 223 224 225
## 47760.09 54934.12 55817.95 78127.03 85471.79 87434.86 94874.03 86006.34
## 226 227 228 229 230 231 232 233
## 80125.07 79715.39 76432.51 75610.82 80252.87 81589.79 76136.81 71445.71
## 234 235 236 237 238 239 240 241
## 76986.02 63901.49 56097.63 48209.64 39989.37 44097.25 46206.82 39788.81
## 242 243 244 245 246 247 248 249
## 33595.09 43671.42 38033.64 41890.44 34309.41 33041.21 36623.22 39390.50
## 250 251 252 253 254 255 256 257
## 30403.81 36222.83 39948.04 45038.67 47702.49 47205.24 57299.67 74446.08
## 258 259 260 261 262 263 264 265
## 74264.84 67709.62 69161.74 65458.30 66875.45 60758.28 50247.92 46354.92
## 266 267 268 269 270 271 272 273
## 46560.13 42744.91 51372.54 47720.34 51783.94 49938.27 53924.09 54214.18
## 274 275 276 277 278 279 280 281
## 60110.97 57792.64 67271.68 61318.26 61524.08 59906.25 65534.00 59390.17
## 282 283 284 285 286 287 288 289
## 56022.91 45784.66 44213.55 61100.33 66519.57 66920.82 64390.18 63495.84
## 290 291 292 293 294 295 296 297
## 67416.60 71248.45 52625.94 42926.40 37057.49 47171.97 50348.82 49480.53
## 298 299 300 301 302 303 304 305
## 73191.83 79018.06 79672.34 84192.34 82427.02 77556.89 80954.23 56356.16
## 306 307 308 309 310 311 312 313
## 52692.22 52378.42 46293.04 43558.42 47089.55 39791.01 38538.72 33209.25
## 314 315 316 317 318 319 320 321
## 36917.95 36115.70 39870.38 37895.20 63164.79 61049.21 62640.27 70478.12
## 322 323 324 325 326 327 328 329
## 72817.61 97788.67 96198.85 72513.34 71335.28 69725.52 61838.50 59018.39
## 330 331 332 333 334 335 336 337
## 29568.91 33182.59 33613.31 35886.76 35240.80 40892.27 41946.33 37289.11
## 338 339 340 341 342 343 344 345
## 36519.27 36643.40 32056.13 37934.95 38585.07 38838.34 39696.60 41446.76
## 346 347 348 349 350 351 352 353
## 43232.26 42988.89 36074.85 26831.59 32069.22 30953.04 30551.30 28215.47
## 354 355 356 357 358 359 360 361
## 32800.66 36479.77 40854.00 39079.26 40314.31 42409.95 49657.12 50196.68
## 362 363 364 365 366 367 368 369
## 50394.31 52808.16 50341.97 49797.63 42590.09 39842.99 36096.50 33919.66
## 370 371 372 373 374 375 376 377
## 30056.37 37198.37 39439.54 47167.20 41259.52 40717.76 39284.35 38826.48
## 378 379 380 381 382 383 384 385
## 29876.97 34383.03 27573.80 35628.60 45754.82 49248.90 47556.83 49508.65
## 386 387 388 389 390 391 392 393
## 55604.30 64899.90 58247.08 52826.01 52560.14 59792.03 60304.93 68800.12
## 394 395 396 397 398 399 400 401
## 58123.81 59659.34 59228.25 58722.44 57239.62 56025.97 43049.55 51463.41
## 402 403 404 405 406 407 408 409
## 50483.83 49470.74 55743.96 48435.64 47760.58 46120.73 41884.45 40738.55
## 410 411 412 413 414 415 416 417
## 38841.15 33053.44 40768.76 43636.56 38415.44 33601.93 48176.13 51944.03
## 418 419 420 421 422 423 424 425
## 55794.71 48414.48 44811.32 43513.78 47028.52 35679.23 35413.85 29787.45
## 426 427 428 429 430 431 432 433
## 35265.51 43426.58 50180.91 47011.03 44138.34 41125.00 41015.42 37563.66
## 434 435 436 437 438 439 440 441
## 33779.26 31068.60 32613.18 34425.59 32470.10 37238.97 43320.43 40137.72
## 442 443 444 445 446 447 448 449
## 39849.59 42795.91 40724.24 44634.00 39975.92 31187.47 30048.18 41178.08
## 450 451 452 453 454 455 456 457
## 40865.98 46404.21 42127.57 42470.24 44058.36 47702.24 37778.90 42531.26
## 458 459 460 461 462 463 464 465
## 38045.99 45463.76 48913.45 51481.63 48276.54 50570.54 50768.04 52480.16
## 466 467 468 469 470 471 472 473
## 51987.81 54872.73 52254.41 57204.64 50599.28 48257.95 46872.35 43564.16
## 474 475 476 477 478 479 480 481
## 47245.23 54559.41 49097.74 50766.94 45659.92 44071.32 46847.65 36473.41
## 482 483 484 485 486 487 488 489
## 30163.63 31995.60 34638.38 36098.58 37045.14 30813.01 43092.84 49619.94
## 490 491 492 493 494 495 496 497
## 56313.73 51130.89 55867.35 63348.47 67083.71 53614.69 44380.85 42445.29
## 498 499 500 501 502 503 504 505
## 42762.03 43537.38 38134.29 40485.28 45680.84 51248.90 51944.39 52052.74
## 506 507 508 509 510 511 512 513
## 45880.54 47193.74 43527.72 46228.10 45900.48 39741.85 40850.33 40041.96
## 514 515 516 517 518 519 520 521
## 41125.49 43712.75 36695.98 32069.71 55481.88 63479.45 67059.38 60570.80
## 522 523 524 525 526 527 528 529
## 61883.51 75197.58 82034.82 57485.80 52458.40 49218.82 53510.25 53026.45
## 530 531 532 533 534 535 536 537
## 43406.28 48298.30 60705.69 55336.11 58665.82 62573.86 59685.39 54815.50
## 538 539 540 541 542 543 544 545
## 48408.49 47091.26 54869.68 54625.09 47333.89 49513.06 49343.19 50023.15
## 546 547 548 549 550 551 552 553
## 40859.13 32858.14 37113.13 45067.41 44868.43 46540.20 40669.82 49502.54
## 554 555 556 557 558 559 560 561
## 50634.74 40618.33 49968.48 57674.01 57051.65 60577.40 56429.30 67954.57
## 562 563 564 565 566 567 568 569
## 84309.38 74598.59 63390.91 67721.72 65883.64 67046.54 58785.18 43094.55
## 570 571 572 573 574 575 576 577
## 49962.98 55749.95 56909.06 58943.06 59577.16 56761.08 68762.69 58348.71
## 578 579 580 581 582 583 584 585
## 52197.30 59646.74 61120.63 54352.86 60494.00 56158.66 53262.36 66560.90
## 586 587 588 589 590 591 592 593
## 52281.07 59478.34 58588.89 52436.75 51755.81 52026.33 42927.99 45668.85
## 594 595 596 597 598 599 600 601
## 40402.85 44563.68 53151.56 46616.27 52357.99 54688.19 60243.78 56478.83
## 602 603 604 605 606 607 608 609
## 61196.21 52933.63 54776.00 55536.79 57798.75 58361.30 57911.26 52205.25
## 610 611 612 613 614 615 616 617
## 59125.52 57224.46 54379.76 51151.80 44255.62 55535.45 59345.04 50461.94
## 618 619 620 621 622 623 624 625
## 60655.67 64756.82 58376.96 80159.44 65580.10 58063.03 60025.85 55472.22
## 626 627 628 629
## 46001.13 56494.11 37439.78 37302.81
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8267
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.848786 0.5563604 3.646223
## t2* 1705.474990 21.4363428 208.465909
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 3.222338 7.989437 15.07923
## 2 lag_depvar 1404.805478 1716.356189 2086.38335Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Oct 16 01:06:37 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Oct 16 01:06:46 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Oct 16 01:06:54 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Oct 16 01:07:03 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Oct 16 01:07:12 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Oct 16 01:07:21 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Oct 16 01:07:30 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Oct 16 01:07:39 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Oct 16 01:07:48 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Oct 16 01:07:57 2023
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 5.251556 | 5.410333 | 5.629750 | 6.7029111 |
| Comida | 365.239889 | 310.278417 | 314.087500 | 345.3436222 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 35.509667 | 47.072333 | 38.297667 | 33.0219111 |
| Enceres | 22.524111 | 20.086417 | 17.443792 | 24.2113778 |
| Farmacia | 2.220000 | 1.831667 | 7.913875 | 8.4078667 |
| Gas/Bencina | 38.292444 | 44.325000 | 28.954333 | 27.7030222 |
| Diosi | 15.216778 | 31.180667 | 41.934250 | 35.3073111 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 6.1048667 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 18.4326222 |
| VTR | 14.661111 | 25.156667 | 22.121792 | 19.8275111 |
| Netflix | 5.148889 | 7.151583 | 7.090167 | 7.0104667 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.8402889 |
| Total | 504.064444 | 499.588167 | 522.488250 | 532.9137778 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2139, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-11-09 00:04:58 sería de: 37.238 pesos// Percentil 95% más alto proyectado: 40.466,95
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36505.90 | 36499.36 |
| Lo.80 | 36554.68 | 36555.03 |
| Point.Forecast | 37238.50 | 38908.42 |
| Hi.80 | 39046.10 | 43642.85 |
| Hi.95 | 40038.24 | 46149.10 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2767 1008.8581
## s.e. 0.1336 29.9259
##
## sigma^2 = 27409: log likelihood = -364.6
## AIC=735.21 AICc=735.67 BIC=741.28paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2540 727.8517 9.0752
## s.e. 0.1354 300.6833 9.6544
##
## sigma^2 = 27513: log likelihood = -364.18
## AIC=736.36 AICc=737.14 BIC=744.46forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 755.1648 | 671.1940 | 712.1943 |
| Lo.80 | 871.5073 | 788.0714 | 795.6624 |
| Point.Forecast | 1091.2836 | 1008.8581 | 980.4709 |
| Hi.80 | 1311.0598 | 1229.6447 | 1265.5960 |
| Hi.95 | 1427.4024 | 1346.5222 | 1448.1455 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.30 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.2 xts_0.13.1
## [13] forecast_8.21.1 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.3 forcats_1.0.0
## [22] dplyr_1.1.3 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0
## [28] sjPlot_2.8.15 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.3 httr_1.4.7
## [34] readxl_1.4.3 zoo_1.8-12 stringr_1.5.0
## [37] stringi_1.7.12 data.table_1.14.8 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.9 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.4
## [16] timechange_0.2.0 anytime_0.3.9 tseries_0.10-54
## [19] colorspace_2.1-0 xfun_0.39 crayon_1.5.2
## [22] jsonlite_1.8.4 lme4_1.1-34 glue_1.6.2
## [25] gtable_0.3.4 emmeans_1.8.8 sjstats_0.18.2
## [28] sjmisc_2.8.9 car_3.1-2 quantmod_0.4.25
## [31] abind_1.4-5 mvtnorm_1.2-3 DBI_1.1.3
## [34] ggeffects_1.3.1 Rcpp_1.0.10 viridisLite_0.4.2
## [37] xtable_1.8-4 performance_0.10.5 bit_4.0.5
## [40] htmlwidgets_1.6.2 timeSeries_4031.107 gplots_3.1.3
## [43] ellipsis_0.3.2 spatial_7.3-14 pkgconfig_2.0.3
## [46] farver_2.1.1 nnet_7.3-16 sass_0.4.5
## [49] dbplyr_2.3.4 janitor_2.2.0 utf8_1.2.3
## [52] tidyselect_1.2.0 labeling_0.4.3 rlang_1.1.0
## [55] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [58] cachem_1.0.7 cli_3.6.1 generics_0.1.3
## [61] sjlabelled_1.2.0 broom_1.0.5 evaluate_0.20
## [64] fastmap_1.1.1 yaml_2.3.7 knitr_1.44
## [67] bit64_4.0.5 caTools_1.18.2 nlme_3.1-153
## [70] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [73] compiler_4.1.2 rstudioapi_0.14 curl_5.1.0
## [76] bslib_0.4.2 fBasics_4031.95 Matrix_1.6-1.1
## [79] its.analysis_1.6.0 nloptr_2.0.3 urca_1.3-3
## [82] vctrs_0.6.1 pillar_1.9.0 lifecycle_1.0.3
## [85] lmtest_0.9-40 jquerylib_0.1.4 estimability_1.4.1
## [88] bitops_1.0-7 insight_0.19.6 R6_2.5.1
## [91] KernSmooth_2.23-20 janeaustenr_1.0.0 codetools_0.2-18
## [94] assertthat_0.2.1 boot_1.3-28 MASS_7.3-54
## [97] gtools_3.9.4 withr_2.5.1 fracdiff_1.5-2
## [100] bayestestR_0.13.1 parallel_4.1.2 hms_1.1.3
## [103] quadprog_1.5-8 timeDate_4022.108 minqa_1.2.6
## [106] snakecase_0.11.1 rmarkdown_2.25 carData_3.0-5
## [109] TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))